We use a subset of 27,520 images from each dataset for training, 11,008 for validation, and 13,760 for inference. We end up with training samples of 55,040 images, since we use two datasets as the train set. The training data is sampled sequentially from the original dataset.
On the other hand, the entire validation and test datasets are sampled from the data loaders as batches.
We use the TorchXRayVision [1]}, [2]} library, which is specialized in handling chest X-ray images, to load the raw data.
